H2020Индивидуална стипендия2019–2025

ERMADA · Illuminating Earth’s microbial diversity and origins from metagenomes with deep learning

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2019-08-01 → 2025-03-21
Финансиране от ЕС: 247 628 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Молекулярните механизми на вирусната инвазия в човешките клетки се анализират чрез биологични и изчислителни инструменти. Разбирането на патогенезата при COVID-19 помага за ограничаване на вредните ефекти от вируса и спасяване на човешки животи.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Illuminating Earth’s microbial diversity and origins from metagenomes with deep learning

The project coincided with the onset of the worldwide crisis due to the COVID-19 pandemic. As a consequence, the scope and direction of the project were modified in order to combat this unknown threat and address the unprecedented health situation with the latest scientific tools. Resources and expertise were directed towards an international collaboration of researchers and groups from France, Greece, Canada, USA, Spain and others, with the aim of uncovering the mechanics of the viral invasion in human cells with a combination of state-of-the-art biological and computational tools. The benefits of understanding the underlying molecular mechanisms of COVID-19 pathogenesis held an immense potential for saving potentially millions of lives and halting the virus' devastating impact on all human activity during the critical first months of the pandemic spread.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The estimated number of microbes on our planet outnumbers the stars of the Milky Way galaxy and their biomass exceeds that of all plants and animals. Out of the 10^12 microbial species, only around 10^4 have been cultured, less than 10^5 species are represented by classified sequences, and a staggering estimated 99% of these microorganisms remain taxonomically unknown. Metagenomic shotgun sequencing has emerged as the most prevalent way of studying and classifying microorganisms from various habitats whereas genome analysis can be used to uncover the functions of genes, enzymes and metabolic pathways in a microbial community. This painstaking effort is crucial to understanding Earth's biodiversity, as microbes play important roles in regulating the planet’s biogeochemical cycles through processes that govern nutrient circulation in both terrestrial and marine environments. In this proposal, we will employ cutting edge bioinformatics and machine learning algorithms to analyze and elucidate Earth’s microbial diversity. We will use deep neural networks trained by large volumes of metagenomic sequences as well as big data methods to process hundreds of terabytes of data and taxonomically classify all uncharacterized metagenomic samples, by identifying their origins and habitats. Going beyond the capacities of conventional sequence similarity and comparison analyses, neural network models can capture higher level, abstract defining features and patterns in metagenomic sequences. The aim of this study is twofold: i) to gain a deeper understanding of the composition and structure of the microbiome at different rank levels and lineages and ii) to provide a complete record of the planet’s present microbial diversity footprint. The latter can serve as a reference dataset for future studies pertaining to microbiome evolution due to climate change or other long-term environmental factors.

Оригинален текст от CORDIS (на английски).

Участници

EREVNITIKO KENTRO VIOIATRIKON EPISTIMON ALEXANDROS FLEMINGK · Vari-AthensКоординаторГърция